{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from utilsP4 import *\n",
      "%matplotlib inline \n",
      "\n",
      "# Carreguem les dades de la borsa mitja\u00e7ant la funci\u00f3 loadStockData\n",
      "data={}\n",
      "#companies=['GOOG','MSFT','IBM','YHOO','FB']\n",
      "companies=['GOOG']\n",
      "for c in companies:\n",
      "    data[c]=loadStockData(c)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class AdWin:\n",
      "    \"\"\"\n",
      "        Finestra lliscant adaptativa que permet trobar la mitja de totes les dades que es\n",
      "        van inserin i que permet trobar canvis bruscos\n",
      "        en elles i adaptar-se a ells a partir d'un valor de confianca indicat per l'usuari.\n",
      "    \"\"\"\n",
      "    def __init__(self, d):\n",
      "        \"\"\"\n",
      "            Constructor de la finestra.\n",
      "\n",
      "            param d Confianca el canvi. Com mes gran mes facil sera detectar un canvi.\n",
      "        \"\"\"\n",
      "        self.data = []\n",
      "        self.length = 0\n",
      "        self.rel = d\n",
      "        self.m = 0.0\n",
      "\n",
      "    def ecut(self, w1, w2):\n",
      "        \"\"\"\n",
      "            Calculo epsilon, segun la cota de Hoeffding, que nos asegura que la evidencia sobre la\n",
      "            diferencia entre las medias es suficiente segun el numero de puntos de cada ventana.\n",
      "        \"\"\"\n",
      "        m = 1.0 / ( (1.0/len(w1)) + (1.0/len(w2)) )\n",
      "        rel_prima = self.rel / float(len(w1) + len(w2))\n",
      "        epsilon = math.sqrt( (1.0/(2.0*m)) * math.log(4.0/rel_prima) )\n",
      "        return math.fabs(np.mean(w1) - np.mean(w2)) >= epsilon\n",
      "\n",
      "    def add(self, x_t):\n",
      "        \"\"\"\n",
      "            Afegeix una nova dada a la finestra i despres de comprovar si hi ha hagut un canvi\n",
      "            actualitza les dades necessaries.\n",
      "\n",
      "            param x_t Nova dada.\n",
      "\n",
      "            return self.m Mitja de les dades del interior de la finestra\n",
      "            return \t0 si no hi ha hagut cap canvi significatiu\n",
      "                    1 si hi ha hagut un canvi i la mitja ha disminuit\n",
      "                    -1 si hi ha hagut un canvi i la mitja ha augmentat\n",
      "        \"\"\"\n",
      "        self.data.append(x_t)\n",
      "        self.length += 1\n",
      "\n",
      "        canvi = 0\n",
      "        idx = 1\n",
      "        while idx < len(self.data):\n",
      "            w1 = self.data[:idx]\n",
      "            w2 = self.data[idx:]\n",
      "\n",
      "            if self.ecut(w1, w2):\n",
      "                self.data = w2[:]\n",
      "                canvi = 2\n",
      "                self.length -= len(w1)\n",
      "                idx = 0\n",
      "\n",
      "            idx += 1\n",
      "        mean = np.mean(self.data)\n",
      "        if canvi == 2:\n",
      "            if self.m > mean:\n",
      "                canvi = 1\n",
      "            elif self.m < mean:\n",
      "                canvi = -1\n",
      "\n",
      "        self.m = mean\n",
      "\n",
      "        return mean, canvi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# PROVA AMB DADES SINTETIQUES\n",
      "\n",
      "# Adatptative window amb d=0.98\n",
      "method1 = AdWin(0.95)\n",
      "\n",
      "v=[] # Genera un mostra de 400 + 600 valors amb un canvi brusc entre 400 i 401.\n",
      "for i in xrange(200):\n",
      "    v.append(0.6+0.1*(random.random()-0.5))\n",
      "for i in xrange(400):\n",
      "    v.append(0.4+0.1*(random.random()-0.5))\n",
      "for i in xrange(600):\n",
      "    v.append(0.1*(random.random()-0.5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "\n",
      "t1 = time.clock()\n",
      "# Anem afegint de forma seq\u00fcencial les dades dins la nostra finestra lliscant.\n",
      "# Guardem el resultat dins la llista output1 i output2\n",
      "output1=[]\n",
      "for item in v:\n",
      "    mean,action=method1.add(item)\n",
      "    output1.append(mean)\n",
      "\n",
      "print \"temps: \", time.clock() - t1\n",
      "  \n",
      "# Visualitzem el resultat\n",
      "pylab.plot(v)\n",
      "pylab.plot(output1,'r') # plot del resultat del M\u00e8tode 1\n",
      "pylab.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "temps:  40.72\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MDGRkZODvf/+7v7dsUwSNGCiJjARuu433KWzfDgwYwAM59enDJ7O99x5vQRAEETL4FY7C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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1f9dbd0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Prova amb dades de la Borsa utilizant l'estrat\u00e8gia b\u00e0sica que us donem\n",
      "method = AdWin(0.95)\n",
      "for item in data:\n",
      "    # Creem un objecte broker. Aquest objecte cada cop que entrem una nova dada dira si hem de comprar o vendre accions\n",
      "    # per_change indica el percentatge de canvi respecte al valor de l'accio per realitzar una compra/venta\n",
      "    # min_time indica el temps minim que ens hem d'esperar per fer nova accio de compra/venta\n",
      "    broker= StockMarketWin(method,0.1,10)\n",
      "    # Executem l'estrtegia de comprar a partir de l'objecte broker i les dades d'entrada. La funci\u00f3 estrategiaBasica \n",
      "    # est\u00e0 definidia dins utilsP4.py. Ella \u00e9s la responsable de decidir la quantatit de compra o venta d'accios a partir de la\n",
      "    # suggeriencia del broker\n",
      "    temp_badget,invested_money,non_strategy= estrategiaBasica(broker,data[item])\n",
      "    # Mostrem els resultats per pantalla\n",
      "    print_results(data[item],temp_badget,invested_money,non_strategy)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "******************************************************************\n",
        "*  \n",
        "*  ---------Valor inicial de la acci\u00f3  100.3   -------------\n",
        "*  ----------------------------------------------------------\n",
        "*  INICI: Diners  =  100000.0\n",
        "*  INICI: Valor accions =  49949.4  ( #accions = 498.0 *Valor Accions= 100.3 )\n",
        "*  INICI: Diners sense invertir =  50050.6\n",
        "*  ----------------------------------------------------------\n",
        "*  ----------------------------------------------------------\n",
        "*  ----------------------------------------------------------\n",
        "*  FINAL: Diners  =  589451.9\n",
        "*  FINAL: Valor accions =  558175.8  ( #accions = 546.0 *Valor Accions= 1022.3 )\n",
        "*  FINAL: Diners sense invertir =  31276.1\n",
        "*  ----------------------------------------------------------\n",
        "*  ----------------------------------------------------------\n",
        "*  Benefici Global=  489451.9\n",
        "*  Benefici produit pel Broker =  30346.5\n",
        "*  ----------------------------------------------------------\n",
        "******************************************************************\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EXLlyBR06dMDq1auRlZUFGxsbAICNjQ2ynt/JMjIy0EXe2hGAUChUKbSRuqGo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\nf8R35sx6pwj98ovi3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       "text": [
        "<matplotlib.figure.Figure at 0x31de210>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}